Data profiling

As you can see from my wonderful author profile above, my name is Scott Drayton. I spend far too much of my spare time sitting inside playing my Xbox. I also mostly use Outlook for my personal email needs, and own a laptop that runs Windows.

Don’t worry, you haven’t accidently clicked on a terrible dating profile. What I am going to illustrate is just how quickly the Single Customer View (SCV) that you need to effectively serve your customers, can become a tangled mess of duplicate records using a brief story about my life.

Many of us in the data industry have become familiar with the term Single Customer View (SCV) over the last decade. Those that have worked on an SCV project will have seen how they can improve our data management processes, save on marketing costs or improve your customer experiences.

However, with many SCVs being focussed on just one of those outcomes, there has sometimes been an air of disappointment with the results. Some organisations have even ended up with multiple SCVs for different purposes that don’t agree when there is an urgent need to bring together data for another reason, such as a Data Subject Access Request (DSAR).

I’m an avid shopper. I know the importance of a seamless, intuitive shopping experience; from finding the product I’m after, through to checkout and any post-sale service. Once I find this, I’ll want to keep coming back for more. The opportunity here for retailers to disrupt, stand out and build loyalty is strong - and other consumers agree. 72% wish that retailers would be more innovative in how they use digital technology to improve their shopping experience. And likewise, 72% of consumers are more likely to shop with retailers that are digitally innovative – up from 60% in 2017. (The Future Shopper, Salmon 2018)

Whilst many businesses understand the challenge for more personalised and seamless experiences, they struggle to cope with the growing volumes of data available. It’s predicted that by 2025 the ‘datasphere’ will grow to 163 zettabytes – ten times the 16.1ZB of data generated in 2016. It’s no surprise that less than one in five (19%) say they are unlocking the full potential of their data to improve their relationship with customers.

This torrent of information is causing an ‘infobesity’ problem. Businesses are faced with an information overload. They have so much data, it can be overwhelming to know what to do with it all. How can they turn that data into useful information, unlocking its value and delivering better outcomes for their customers?

With 2018 now live and kicking, I decided to take a look back at what subjects proved most popular in 2017 based on our blog. With GDPR coming up fast in May, interest in Data Regulations has unsurprisingly jumped up the list, but read on to take a look at what other subjects piqued your curiosity and delve into some of the best articles from last year.

If you are a data quality professional then you have more than likely heard the terms Data Lake, Data Swamp, Data Ocean and even Data Pond and Data Puddle. In fact, stick the word ‘data’ in front of any word used to name a body of water and you’ve more than likely found a commonly used term in the industry (although I have yet to hear of a Data Paddling Pool’). As the gatekeeper of our ever-growing Glossary section, I have picked out some of the most commonly mistaken terms – and with help from our team of experts, I’ve explained how we define them.

Many of us are aware of the benefits that high-quality data can bring to an organisation including improvements in operational efficiencies, better decision making and avoidance of risk. It’s getting started that can often be the biggest road block. By that I mean, if you can’t quantify the tangible returns that investment in data quality can bring, how do you get buy-in for investment in it?

Traditionally, organisations have tackled their SCV requirement through the deployment of an MDM platform. And yet, as Philip discusses in his paper, ‘MDM has always been complex, costly and time-consuming to implement’ and so not necessarily, therefore, in tune with modern business requirements. Layer in an increase in regulation and we have a perfect storm of reasons for organisations to seek an alternative route.

So, what options are there for organisations looking to keep costs to a minimum or take a more agile approach to developing an SCV?

The proliferation of data provides us with both challenges, and, if managed correctly, great opportunities. So where to start? It’s all about being able to get a full and complete view of your data. Without the ability to bring relevant data elements together into a single view it’s simply impossible to ‘see the wood for the trees’.

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